Extrapolating Away from the Cutoff in Regression Discontinuity Designs
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回归不连续设计中远离阈值的效应外推
- 研究问题
- 回归不连续设计通常只识别阈值附近效应，如何在额外假设下约束未观测反事实的变化，从而识别远离阈值的处理效应范围？
- 方法与思路
- 作者利用数据与敏感性参数限制反事实结局的变化，构造适用于单阈值和多阈值设计的部分识别，并建议结合制度背景选择假设。
- 材料陈述的贡献
- 摘要称，所提假设弱于既有文献并得到集合识别结果；模拟与应用展示了其对处理效应符号判断的作用，作者认为结论更可信。
- 阅读关注点
- 应核对各类外推限制的数学形式、与既有假设的强弱关系，以及敏感性参数取值如何改变识别集合和效应符号的判断。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未列出具体限制与识别界，外推可信度仍取决于应用背景和参数选择。
原文摘要
Canonical RD designs yield credible local estimates of the treatment effect at the cutoff under mild continuity assumptions, but they fail to identify treatment effects away from the cutoff without additional assumptions. The fundamental challenge of identifying treatment effects away from the cutoff is that the counterfactual outcome under the alternative treatment status is never observed. This paper aims to provide a methodological blueprint to identify treatment effects away from the cutoff in various empirical settings by offering a non-exhaustive list of assumptions on the counterfactual outcome. Instead of assuming the exact evolution of the counterfactual outcome, this paper bounds its variation using the data and sensitivity parameters. The proposed assumptions are weaker than those introduced previously in the literature, resulting in partially identified treatment effects that are less susceptible to assumption violations. This approach accommodates both single cutoff and multi-cutoff designs. The specific choice of the extrapolation assumption depends on the institutional background of each empirical application. Additionally, researchers are recommended to conduct sensitivity analysis on the chosen parameter and assess resulting shifts in conclusions. The paper compares the proposed identification results with results using previous methods via an empirical application and simulated data. It demonstrates that set identification yields a more credible conclusion about the sign of the treatment effect.